An East Asian (EA) written language (e.g., Chinese, Japanese, Korean, or the like) is very complex having thousands of characters. For example, the Chinese written language may include over 20,000 to 50,000 single characters and 10,000 commonly used characters.
This huge and complex character set makes entry of characters, by a typical keyboard into computers or electronic devices very slow and cumbersome. While the entry of East Asian characters is difficult on character-set keyboards, the problem is exacerbated when moving to handheld devices that are commonly equipped with input mechanisms with fewer keys. For instance, a personal digital assistant commonly uses only eight keys to enter information. As a result, attempting to enter 10,000-20,000 Chinese characters with a few keys on the personal digital assistant, can be very time consuming and awkward.
A practical way to enter characters into a computing system is by using a stylus or an electronic pen in conjunction with a tablet-based personal computing device. This method of entry is known as online handwriting recognition. Accordingly, this method may be used to enter East Asian characters, but may face problems during the process.
With online handwriting recognition, problems arise such as lack of speed, accuracy, different writing styles, and different writing orders in recognizing on-line East Asian handwriting. Hidden Markov models (HMMs) have been applied towards online handwriting recognition. HMMs may automatically extract knowledge from training patterns and have the capability of modeling temporal information. By using the intrinsic properties of HMMs for online handwriting recognition, the underlining structure of EA characters may be modeled and sequential information can be modeled according to time. For example, online handwriting recognition is a mainstream of time sequential data for input to computers. Therefore, HMMs can model variability and temporal information of East Asian character handwriting data.